Distributional value coding and reinforcement learning in the brain

摘要 对环境中未来的回报做出预测，并采取行动获得这些回报，这是至关重要的。 为了生存。当这些预测过于乐观时--例如，在赌瘾的情况下--或者 过度悲观--如焦虑和抑郁--不适应行为可能导致和表现出显著的 疾病负担。做出奖励预测的一个根本挑战是，世界本身就是随机的， 分布尾部的事件不必反映平均值。因此，预测不是很有用的 不仅是平均值，而且还有即将到来的奖励的完全概率分布。事实上，最近在这方面的进展 机器学习已经证明，实现从平均奖励到完全奖励的转变 分发可以显著提高复杂任务域中的性能。尽管它看起来很复杂， 这种“分布式强化学习”在计算上可以用非常简单和 生物学上合理的学习规则。最近的一项研究发现，多巴胺神经元活性的结构可能是 与分布式强化学习一致，但尚不清楚额外的神经元回路是否 受累--最显著的是腹侧纹状体(VS)和眶前叶皮质(OFC)，两者都接受多巴胺 投入，并被认为代表预期的回报，也称为“价值”。在这里，我们建议调查 这些下游区域的值编码是否与分布式强化学习一致。在……里面 特别是，当老鼠用气味和水进行经典的条件反射时，我们将从这些大脑区域进行记录 奖励。在第一个任务中，我们将保持平均奖励不变，同时改变奖励方差或更高- 对矩进行排序，并询问VS和OFC中的神经元是否代表超出平均值的信息， 与分布式强化学习一致。原则上，这应该使我们能够解码完整的 奖赏分配纯粹来自神经活动。在第二个任务中，我们将向老鼠呈现一组气味 以不同的概率预测相同的奖励金额。这些伯努利分布的简单性将 使我们能够比较大脑中长期存在的种群编码理论--也就是概率分布 可以在神经活动中实例化来指导行为。除了高密度硅探针记录外，我们还 将在这些任务中进行双光子钙成像，以评估基因和分子上的不同 纹状体神经元亚群对分布性强化学习的贡献不同。最后， 我们将结合这些记录和纹状体中多巴胺动态的同步成像来研究 多巴胺影响体内的纹状体活动。总之，这些研究将有助于阐明多巴胺在学习中的作用 成瘾、焦虑、抑郁和双相情感障碍的奖赏分配及其失调。

ABSTRACT Making predictions about future rewards in the environment, and taking actions to obtain those rewards, is critical for survival. When these predictions are overly optimistic — for example, in the case of gambling addiction — or overly pessimistic — as in anxiety and depression — maladaptive behavior can result and present a significant disease burden. A fundamental challenge for making reward predictions is that the world is inherently stochastic, and events on the tails of a distribution need not reflect the average. Therefore, it may be useful to predict not only the mean, but also the complete probability distribution of upcoming rewards. Indeed, recent advances in machine learning have demonstrated that making this shift from the average reward to the complete reward distribution can dramatically improve performance in complex task domains. Despite its apparent complexity, such “distributional reinforcement learning” can be achieved computationally with a remarkably simple and biologically plausible learning rule. A recent study found that the structure of dopamine neuron activity may be consistent with distributional reinforcement learning, but it is unknown whether additional neuronal circuity is involved — most notably the ventral striatum (VS) and orbitofrontal cortex (OFC), both of which receive dopamine input and are thought to represent anticipated reward, also called “value”. Here, we propose to investigate whether value coding in these downstream regions is consistent with distributional reinforcement learning. In particular, we will record from these brain regions while mice perform classical conditioning with odors and water rewards. In the first task, we will hold the mean reward constant while changing the reward variance or higher- order moments, and ask whether neurons in the VS and OFC represent information over and above the mean, consistent with distributional reinforcement learning. In principle, this should enable us to decode the complete reward distribution purely from neural activity. In the second task, we will present mice with a panel of odors predicting the same reward amount with differing probabilities. The simplicity of these Bernoulli distributions will allow us to compare longstanding theories of population coding in the brain — that is, how probability distributions can be instantiated in neural activity to guide behavior. In addition to high-density silicon probe recordings, we will perform two-photon calcium imaging in these tasks to assess whether genetically and molecularly distinct subpopulations of neurons in the striatum contribute differentially to distributional reinforcement learning. Finally, we will combine these recordings with simultaneous imaging of dopamine dynamics in the striatum to ask how dopamine affects striatal activity in vivo. Together, these studies will help clarify dopamine’s role in learning distributions of reward, as well as its dysregulation in addiction, anxiety, depression, and bipolar disorder.